Weak invariance principle for finite-populationU-statistics

Under the weakest possible conditions, we establish the weak invariance principle for finite-populationU-statistics in this paper. It is worth while to point out that, for the sampling without replacement, the sequence of random delements inC[0, 1], associated with the sample partial sums or theU-statistics, converges in law to the standard Brown bridge, but not to the Brown motion as in the usual case of replacement sampling.     

Normal approximation for a random elliptic equation

We consider solutions of an elliptic partial differential equation in \(\mathbb{R }^d\) with a stationary, random conductivity coefficient that is also periodic with period \(L\) . Boundary conditions on a square domain of width \(L\) are arranged so that the solution has a macroscopic unit gradient. We then consider the average flux that results from this imposed boundary condition. It is known that in the limit \(L \rightarrow \infty \) , this quantity converges to a deterministic constant, almost surely. Our main result is that the law of this random variable is very close to that of a normal random variable, if the domain size \(L\) is large. We quantify this approximation by an error estimate in total variation. The error estimate relies on a second order Poincaré inequality developed recently by Chatterjee.     

Normal approximation of random permanents

For random permanents, we obtain an estimate of the rate of convergence in the central limit theorem.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 47, No. 7, pp. 922–927, July, 1995.     

Normal approximation and fourth moment theorems for monochromatic triangles

Given a graph sequence denote by T₃(G_n) the number of monochromatic triangles in a uniformly random coloring of the vertices of G_n with colors. In this paper we prove a central limit theorem (CLT) for T₃(G_n) with explicit error rates, using a quantitative version of the martingale CLT. We then relate this error term to the well-known fourth-moment phenomenon, which, interestingly, holds only when the number of colors satisfies . We also show that the convergence of the fourth moment is necessary to obtain a Gaussian limit for any , which, together with the above result, implies that the fourth-moment condition characterizes the limiting normal distribution of T₃(G_n), whenever . Finally, to illustrate the promise of our approach, we include an alternative proof of the CLT for the number of monochromatic edges, which provides quantitative rates for the results obtained in [7].     

The not-quite non-atomic game: Normal approximation

Measure games with a large number of players are frequently approximated by nonatomic games. In fact, however, while it is true that values of large measure games will, under certain reasonable circumstances, converge to the value of a non-atomic game, it is also true that this convergence is quite slow. Using the multi-linear extension and the central limit theorem, we obtain an approximation which (because it is based on the normal distribution) we call the normal approximation. We show that, for two examples with several hundred and several thousand players respectively, the normal approximation is much better than the non-atomic approximation.     

Normal cone approximation and offset shape isotopy

This work addresses the problem of the approximation of the normals of the offsets of general compact sets in Euclidean spaces. It is proven that for general sampling conditions, it is possible to approximate the gradient vector field of the distance to general compact sets. These conditions involve the μ-reach of the compact set, a recently introduced notion of feature size. As a consequence, we provide a sampling condition that is sufficient to ensure the correctness up to isotopy of a reconstruction given by an offset of the sampling. We also provide a notion of normal cone to general compact sets that is stable under perturbation.     

Normal approximation for linear stochastic processes and random fields in Hilbert space

The central limit theorem is proved for linear random fields defined on an integer-valued lattice of arbitrary dimension and taking values in Hilbert space. It is shown that the conditions in the central limit theorem are optimal. Translated fromMatematicheskie Zametki, Vol. 68, No. 3, pp. 421–428, September, 2000.     

Normal approximation of a functional of a Gaussian field

The asymptotic normality of a functional of a strongly correlated Gaussian random field having the meaning of the random volume of a bounded realization of the field over a parallelepiped or triangle is established in this paper. In addition some problems of geometric probabilities are solved: the distribution density of the distance between independent random vectors having uniform distribution in a rectangle or triangle is found.Translated from Teoriya Sluchainykh Protsessov, No. 16, pp. 55–60, 1988.     

Normal correlation coefficient of non-normal variables using piece-wise linear approximation

The correlation coefficient of non-normal variables is expressed as a function of the correlation coefficient of normal variables using piece-wise linear approximation of each univariate transform of normal to anything, and the second order moments of a multiply truncated bivariate normal distribution. For the inverse problem, an algorithm iterates this analytic function in order to assign a normal correlation coefficient to two non-normal variables. The algorithm is applied for the generation of randomized bivariate samples with given correlation coefficient and marginal distributions and used in a randomization test for bivariate nonlinearity. The test correctly does not reject the null hypothesis of linear correlation if the nonlinearity is plausible and due to the sample transform alone.     

Normal spectral approximation inC *-algebras and in von Neumann-algebras

In this paper the general problem of normal spectral approximation in a unitalC ^*-algebraA is studied. If Δ is a closed and convex subset of the complex plane then the distance from a normal elementa ofA to the setN _A (Δ) of all normal elements with spectrum in Δ equals the one-sided Hausdorff distance from σ(a) to Δ. As a consequence every normal elementa has anN _A (Δ)-approximant which is a function ofa. Furthermore the approximant is unique whenever every point of σ (a) has the same distance to Δ. These results are shown for the approximation in theC ^*-norm as well as in another topological equivalent norm.

     

Poisson approximation for large deviations

Upper and lower bounds are given for P(S ≤ k), 0 ≤ k ≤ ES, where S is a sum of indicator variables with a special structure, which appears, for example, in subgraph counts in random graphs. in typical cases, these bounds are close to the corresponding probabilities for a Poisson distribution with the same mean as S. There are no corresponding general bounds for P(S ≥ k), k > ES, but some partial results are given.     

An algorithm for segment approximation

Summary An algorithm for computing a set of knots which is optimal for the segment approximation problem is developed. The method yields a sequence of real numbers which converges to the minimal deviation and a corresponding sequence of knot sets. This sequence splits into at most two subsequences which converge to leveled sets of knots. Such knot sets are optimal. Numerical results concerning piecewise polynomial approximation are given.     

Estimates for diophantine approximation constants

It is proved that the three-dimensional Diophantine approximation constant is at least $\text{2(275)}{}^{\mathrm{<mtext>?1</mtext><mtext>2</mtext>}}$. This exactly doubles the classical lower bound due to Furtwängler.     

Good points for diophantine approximation

Given a sequence (x _n ) _n=1^∞ of real numbers in the interval [0, 1) and a sequence (δ _n ) _n=1^∞ of positive numbers tending to zero, we consider the size of the set of numbers in [0, 1] which can be ‘well approximated’ by terms of the first sequence, namely, those y ∈ [0, 1] for which the inequality |y − x _n | < δ _n holds for infinitely many positive integers n. We show that the set of ‘well approximable’ points by a sequence (x _n ) _n=1^∞, which is dense in [0, 1], is ‘quite large’ no matter how fast the sequence (δ _n ) _n=1^∞ converges to zero. On the other hand, for any sequence of positive numbers (δ _n ) _n=1^∞ tending to zero, there is a well distributed sequence (x _n ) _n=1^∞ in the interval [0, 1] such that the set of ‘well approximable’ points y is ‘quite small’.     

Newton's method for fractal approximation

The problem of fitting a given function in theL ^q norm with a function generated by an iterated function system can be rapidly solved by applying Newton's method on the parameter space of the iterated function system. The key to this is a method for calculating the derivatives of a potential function with respect to the parameters.